# TACO: TActile World Model as a Self-COrrector for Scalable Robot Policy Post-Training arXiv: https://arxiv.org/abs/2607.02840 · HTML: https://mlsanity.com/paper/2607.02840/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Placed in Embodied and robot world models, one of 11 directions in World models. - Builds on 34 of its 51 references that are in the map. - First posted on arXiv 3 Jul 2026. ## Placed in - World models / [Embodied and robot world models](https://mlsanity.com/wm/embodied-and-robot-world-models/) - Vision-language-action models / [Tactile, force-aware and humanoid VLA](https://mlsanity.com/vla/tactile-force-aware-and-humanoid-vla/) ## Builds on (most cited first, 30 of 34) - [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022) - [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025) - [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025) - [AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems](https://mlsanity.com/paper/2503.06669/) (2025) - [π*0.6: a VLA That Learns From Experience](https://mlsanity.com/paper/2511.14759/) (2025) - [DreamGen: Unlocking Generalization in Robot Learning through Video World Models](https://mlsanity.com/paper/2505.12705/) (2025) - [A Vision-Language-Action-Critic Model for Robotic Real-World Reinforcement Learning](https://mlsanity.com/paper/2509.15937/) (2025) - [WMPO: World Model-based Policy Optimization for Vision-Language-Action Models](https://mlsanity.com/paper/2511.09515/) (2025) - [World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training](https://mlsanity.com/paper/2509.24948/) (2025) - [Diffusion Guidance Is a Controllable Policy Improvement Operator](https://mlsanity.com/paper/2505.23458/) (2025) - [WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL](https://mlsanity.com/paper/2602.13977/) (2026) - [VLAW: Iterative Co-Improvement of Vision-Language-Action Policy and World Model](https://mlsanity.com/paper/2602.12063/) (2026) - [Robo-Dopamine: General Process Reward Modeling for High-Precision Robotic Manipulation](https://mlsanity.com/paper/2512.23703/) (2025) - [World-VLA-Loop: Closed-Loop Learning of Video World Model and VLA Policy](https://mlsanity.com/paper/2602.06508/) (2026) - [OmniVTA: Visuo-Tactile World Modeling for Contact-Rich Robotic Manipulation](https://mlsanity.com/paper/2603.19201/) (2026) - [World4RL: Diffusion World Models for Policy Refinement with Reinforcement Learning for Robotic Manipulation](https://mlsanity.com/paper/2509.19080/) (2025) - [Interactive World Simulator for Robot Policy Training and Evaluation](https://mlsanity.com/paper/2603.08546/) (2026) - [Visuo-Tactile World Models](https://mlsanity.com/paper/2602.06001/) (2026) - [World-Gymnast: Training Robots with Reinforcement Learning in a World Model](https://mlsanity.com/paper/2602.02454/) (2026) - [GigaBrain-0.5M*: a VLA That Learns From World Model-Based Reinforcement Learning](https://mlsanity.com/paper/2602.12099/) (2026) - [AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation](https://mlsanity.com/paper/2507.12768/) (2025) - [Tactile-VLA: Unlocking Vision-Language-Action Model's Physical Knowledge for Tactile Generalization](https://mlsanity.com/paper/2507.09160/) (2025) - [TacVLA: Contact-Aware Tactile Fusion for Robust Vision-Language-Action Manipulation](https://mlsanity.com/paper/2603.12665/) (2026) - [Dream-Tac: A Unified Tactile World Action Model for Contact-Rich Robot Manipulation](https://mlsanity.com/paper/2606.08737/) (2026) - [Learning to Feel the Future: DreamTacVLA for Contact-Rich Manipulation](https://mlsanity.com/paper/2512.23864/) (2025) - [Video2Act: A Dual-System Video Diffusion Policy with Robotic Spatio-Motional Modeling](https://mlsanity.com/paper/2512.03044/) (2025) - [TacForeSight: Force-Guided Tactile World Model for Contact-Rich Manipulation](https://mlsanity.com/paper/2606.11184/) (2026) - [Hi-WM: Human-in-the-World-Model for Scalable Robot Post-Training](https://mlsanity.com/paper/2604.21741/) (2026) - [HarmoWAM: Harmonizing Generalizable and Precise Manipulation via Adaptive World Action Models](https://mlsanity.com/paper/2605.10942/) (2026) - [AT-VLA: Adaptive Tactile Injection for Enhanced Feedback Reaction in Vision-Language-Action Models](https://mlsanity.com/paper/2605.07308/) (2026) ## Built on by (most cited first, 1 of 1) - [TacDyn-WAM: Learning Implicit Tactile Dynamics in a Heterogeneous Visuo-Tactile World Action Model](https://mlsanity.com/paper/2610.00638/) (2026) ## Abstract (arXiv) Vision-Language-Action models and World Action Models have shown promising generalization in robotic manipulation but remain fragile in contact-rich tasks, where contact perturbations can cause failures that are difficult to detect from vision alone. Corrective post-training with tactile feedback can improve recovery, but scaling such supervision through human intervention is costly. World models can synthesize additional training data, yet vision-only generation may produce visually plausible but contact-inconsistent trajectories. We therefore introduce TACO, a scalable robot policy post-training framework built on a compositional tactile world model. Given real rollouts, TACO follows a Recognize--Imagine--Label loop: an inverse dynamics and value model identifies failure-adjacent states using progress estimates, a visuo-tactile generation model imagines local corrections by jointly generating video and tactile sequences, and the inverse dynamics and value model labels them with corrective actions and progress scores. Candidates are filtered for kinematic feasibility and tactile plausibility, then selected by predicted progress gain. TACO aggregates demonstrations, real rollouts, and selected corrections for iterative post-training. It combines knowledge-insulated tactile adaptation with CFG-RL using binary advantage labels while keeping the pretrained VLM backbone fixed. Experiments on real-world tasks show that TACO improves the average task score from 0.375 to 0.825 after two post-training iterations.